cm
CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.
Best use case
cm is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.
CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "cm" skill to help with this workflow task. Context: CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cm/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cm Compares
| Feature / Agent | cm | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# CM - CASS Memory System
Procedural memory for AI coding agents. Transforms scattered sessions into persistent, cross-agent memory. Uses a three-layer cognitive architecture that mirrors human expertise development.
## Why This Exists
AI coding agents accumulate valuable knowledge but it's:
- **Trapped in sessions** - Context lost when session ends
- **Agent-specific** - Claude doesn't know what Cursor learned
- **Unstructured** - Raw logs aren't actionable guidance
- **Subject to collapse** - Naive summarization loses critical details
You've solved auth bugs three times this month across different agents. Each time you started from scratch.
CM solves this with cross-agent learning: a pattern discovered in Cursor is immediately available to Claude Code.
---
## Three-Layer Cognitive Architecture
```
┌─────────────────────────────────────────────────────────────────────┐
│ EPISODIC MEMORY (cass) │
│ Raw session logs from all agents — the "ground truth" │
│ Claude Code │ Codex │ Cursor │ Aider │ PI │ Gemini │ ChatGPT │ ...│
└───────────────────────────┬─────────────────────────────────────────┘
│ cass search
▼
┌─────────────────────────────────────────────────────────────────────┐
│ WORKING MEMORY (Diary) │
│ Structured session summaries: accomplishments, decisions, etc. │
└───────────────────────────┬─────────────────────────────────────────┘
│ reflect + curate (automated)
▼
┌─────────────────────────────────────────────────────────────────────┐
│ PROCEDURAL MEMORY (Playbook) │
│ Distilled rules with confidence tracking and decay │
└─────────────────────────────────────────────────────────────────────┘
```
Every agent's sessions feed the shared memory. A pattern discovered in Cursor **automatically** helps Claude Code on the next session.
---
## The One Command You Need
```bash
cm context "<your task>" --json
```
**Run this before starting any non-trivial task.** Returns:
- **relevantBullets** - Rules from playbook scored by task relevance
- **antiPatterns** - Things that have caused problems
- **historySnippets** - Past sessions (yours and other agents')
- **suggestedCassQueries** - Deeper investigation searches
### Filtering History by Source
`historySnippets[].origin.kind` is `"local"` or `"remote"`. Remote hits include `origin.host`:
```json
{
"historySnippets": [
{
"source_path": "~/.claude/sessions/session-001.jsonl",
"origin": { "kind": "local" }
},
{
"source_path": "/home/user/.codex/sessions/session.jsonl",
"origin": { "kind": "remote", "host": "workstation" }
}
]
}
```
---
## Confidence Decay System
Rules aren't immortal. Confidence decays without revalidation:
| Mechanism | Effect |
|-----------|--------|
| **90-day half-life** | Confidence halves every 90 days without feedback |
| **4x harmful multiplier** | One mistake counts 4× as much as one success |
| **Maturity progression** | `candidate` → `established` → `proven` |
### Score Decay Visualization
```
Initial score: 10.0 (10 helpful marks today)
After 90 days (half-life): 5.0
After 180 days: 2.5
After 270 days: 1.25
After 365 days: 0.78
```
### Effective Score Formula
```typescript
effectiveScore = decayedHelpful - (4 × decayedHarmful)
// Where decay factor = 0.5 ^ (daysSinceFeedback / 90)
```
### Maturity State Machine
```
┌──────────┐ ┌─────────────┐ ┌────────┐
│ candidate│──────▶│ established │───▶│ proven │
└──────────┘ └─────────────┘ └────────┘
│ │ │
│ │ (harmful >25%) │
│ ▼ │
│ ┌─────────────┐ │
└────────────▶│ deprecated │◀─────────┘
└─────────────┘
```
**Transition Rules:**
| Transition | Criteria |
|------------|----------|
| `candidate` → `established` | 3+ helpful, harmful ratio <25% |
| `established` → `proven` | 10+ helpful, harmful ratio <10% |
| `any` → `deprecated` | Harmful ratio >25% OR explicit deprecation |
---
## Anti-Pattern Learning
Bad rules don't just get deleted. They become warnings:
```
"Cache auth tokens for performance"
↓ (3 harmful marks)
"PITFALL: Don't cache auth tokens without expiry validation"
```
When a rule is marked harmful multiple times (>50% harmful ratio with 3+ marks), it's automatically inverted into an anti-pattern.
---
## ACE Pipeline (How Rules Are Created)
```
Generator → Reflector → Validator → Curator
```
| Stage | Role | LLM? |
|-------|------|------|
| **Generator** | Pre-task context hydration (`cm context`) | No |
| **Reflector** | Extract patterns from sessions (`cm reflect`) | Yes |
| **Validator** | Evidence gate against cass history | Yes |
| **Curator** | Deterministic delta merge | **No** |
**Critical:** Curator has NO LLM to prevent context collapse from iterative drift. LLMs propose patterns; deterministic logic manages them.
### Scientific Validation
Before a rule joins your playbook, it's validated against cass history:
```
Proposed rule: "Always check token expiry before auth debugging"
↓
Evidence gate: Search cass for sessions where this applied
↓
Result: 5 sessions found, 4 successful outcomes → ACCEPT
```
Rules without historical evidence are flagged as candidates until proven.
---
## Commands Reference
### Context Retrieval (Primary Workflow)
```bash
# THE MAIN COMMAND - run before non-trivial tasks
cm context "implement user authentication" --json
# Limit results for token budget
cm context "fix bug" --json --limit 5 --no-history
# With workspace filter
cm context "refactor" --json --workspace /path/to/project
# Self-documenting explanation
cm quickstart --json
# System health
cm doctor --json
cm doctor --fix # Auto-fix issues
# Find similar rules
cm similar "error handling best practices"
```
### Playbook Management
```bash
cm playbook list # All rules
cm playbook get b-8f3a2c # Rule details
cm playbook add "Always run tests first" # Add rule
cm playbook add --file rules.json # Batch add from file
cm playbook add --file rules.json --session /path/session.jsonl # Track source
cm playbook remove b-xyz --reason "Outdated" # Remove
cm playbook export > backup.yaml # Export
cm playbook import shared.yaml # Import
cm playbook bootstrap react # Apply starter to existing
cm top 10 # Top effective rules
cm stale --days 60 # Rules without recent feedback
cm why b-8f3a2c # Rule provenance
cm stats --json # Playbook health metrics
```
### Learning & Feedback
```bash
# Manual feedback
cm mark b-8f3a2c --helpful
cm mark b-xyz789 --harmful --reason "Caused regression"
cm undo b-xyz789 # Revert feedback
# Session outcomes (positional: status, rules)
cm outcome success b-8f3a2c,b-def456
cm outcome failure b-x7k9p1 --summary "Auth approach failed"
cm outcome-apply # Apply to playbook
# Reflection (usually automated)
cm reflect --days 7 --json
cm reflect --session /path/to/session.jsonl # Single session
cm reflect --workspace /path/to/project # Project-specific
# Validation
cm validate "Always check null before dereferencing"
# Audit sessions against rules
cm audit --days 30
# Deprecate permanently
cm forget b-xyz789 --reason "Superseded by better pattern"
```
### Onboarding (Agent-Native)
Zero-cost playbook building using your existing agent:
```bash
cm onboard status # Check progress
cm onboard gaps # Category gaps
cm onboard sample --fill-gaps # Prioritized sessions
cm onboard sample --agent claude --days 14 # Filter by agent/time
cm onboard sample --workspace /path/project # Filter by workspace
cm onboard sample --include-processed # Re-analyze sessions
cm onboard read /path/session.jsonl --template # Rich context
cm onboard mark-done /path/session.jsonl # Mark processed
cm onboard reset # Start fresh
```
### Trauma Guard (Safety System)
```bash
cm trauma list # Active patterns
cm trauma add "DROP TABLE" --description "Mass deletion" --severity critical
cm trauma heal t-abc --reason "Intentional migration"
cm trauma remove t-abc
cm trauma scan --days 30 # Scan for traumas
cm trauma import shared-traumas.yaml
cm guard --install # Claude Code hook
cm guard --git # Git pre-commit hook
cm guard --install --git # Both
cm guard --status # Check installation
```
### System Commands
```bash
cm init # Initialize
cm init --starter typescript # With template
cm init --force # Reinitialize (creates backup)
cm starters # List templates
cm serve --port 3001 # MCP server
cm usage # LLM cost stats
cm privacy status # Privacy settings
cm privacy enable # Enable cross-agent enrichment
cm privacy disable # Disable enrichment
cm project --format agents.md # Export for AGENTS.md
```
---
## Starter Playbooks
Starting with an empty playbook is daunting. Starters provide curated best practices:
```bash
cm starters # List available
cm init --starter typescript # Initialize with starter
cm playbook bootstrap react # Apply to existing playbook
```
### Built-in Starters
| Starter | Focus | Rules |
|---------|-------|-------|
| **general** | Universal best practices | 5 |
| **typescript** | TypeScript/Node.js patterns | 4 |
| **react** | React/Next.js development | 4 |
| **python** | Python/FastAPI/Django | 4 |
| **node** | Node.js/Express services | 4 |
| **rust** | Rust service patterns | 4 |
### Custom Starters
Create YAML files in `~/.cass-memory/starters/`:
```yaml
# ~/.cass-memory/starters/django.yaml
name: django
description: Django web framework best practices
bullets:
- content: "Always use Django's ORM for database operations"
category: database
maturity: established
tags: [django, orm]
```
---
## Inline Feedback (During Work)
Leave feedback in code comments. Parsed during reflection:
```typescript
// [cass: helpful b-8f3a2c] - this rule saved me from a rabbit hole
// [cass: harmful b-x7k9p1] - this advice was wrong for our use case
```
---
## Agent Protocol
```
1. START: cm context "<task>" --json
2. WORK: Reference rule IDs when following them (e.g., "Following b-8f3a2c...")
3. FEEDBACK: Leave inline comments when rules help/hurt
4. END: Just finish. Learning happens automatically.
```
**You do NOT need to:**
- Run `cm reflect` (automation handles this)
- Run `cm mark` manually (use inline comments)
- Manually add rules to the playbook
---
## Gap Analysis Categories
| Category | Keywords |
|----------|----------|
| `debugging` | error, fix, bug, trace, stack |
| `testing` | test, mock, assert, expect, jest |
| `architecture` | design, pattern, module, abstraction |
| `workflow` | task, CI/CD, deployment |
| `documentation` | comment, README, API doc |
| `integration` | API, HTTP, JSON, endpoint |
| `collaboration` | review, PR, team |
| `git` | branch, merge, commit |
| `security` | auth, token, encrypt, permission |
| `performance` | optimize, cache, profile |
**Category Status Thresholds:**
| Status | Rule Count | Priority |
|--------|------------|----------|
| `critical` | 0 rules | High |
| `underrepresented` | 1-2 rules | Medium |
| `adequate` | 3-10 rules | Low |
| `well-covered` | 11+ rules | None |
---
## Trauma Guard: Safety System
The "hot stove" principle—learn from past incidents and prevent recurrence.
### How It Works
```
Session History Trauma Registry Runtime Guard
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ rm -rf /* (oops)│ ──────▶ │ Pattern: rm -rf │ ──────▶ │ BLOCKED: This │
│ "sorry, I made │ scan │ Severity: FATAL │ hook │ command matches │
│ a mistake..." │ │ Session: abc123 │ │ a trauma pattern│
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
### Built-in Doom Patterns (20+)
| Category | Examples |
|----------|----------|
| **Filesystem** | `rm -rf /`, `rm -rf ~`, recursive deletes |
| **Database** | `DROP DATABASE`, `TRUNCATE`, `DELETE FROM` without WHERE |
| **Git** | `git push --force` to main/master, `git reset --hard` |
| **Infrastructure** | `terraform destroy -auto-approve`, `kubectl delete namespace` |
| **Cloud** | `aws s3 rm --recursive`, destructive CloudFormation |
### Pattern Storage
| Scope | Location | Purpose |
|-------|----------|---------|
| **Global** | `~/.cass-memory/traumas.jsonl` | Personal patterns |
| **Project** | `.cass/traumas.jsonl` | Commit to repo for team |
### Pattern Lifecycle
- **Active**: Blocks matching commands
- **Healed**: Temporarily bypassed (with reason and timestamp)
- **Deleted**: Removed (can be re-added)
---
## MCP Server
Run as MCP server for agent integration:
```bash
# Local-only (recommended)
cm serve --port 3001
# With auth token (for non-loopback)
MCP_HTTP_TOKEN="<random>" cm serve --host 0.0.0.0 --port 3001
```
### Tools Exposed
| Tool | Purpose | Parameters |
|------|---------|------------|
| `cm_context` | Get rules + history | `task, limit?, history?, days?, workspace?` |
| `cm_feedback` | Record feedback | `bulletId, helpful?, harmful?, reason?` |
| `cm_outcome` | Record session outcome | `sessionId, outcome, rulesUsed?` |
| `memory_search` | Search playbook/cass | `query, scope?, limit?, days?` |
| `memory_reflect` | Trigger reflection | `days?, maxSessions?, dryRun?` |
### Resources Exposed
| URI | Purpose |
|-----|---------|
| `cm://playbook` | Current playbook state |
| `cm://diary` | Recent diary entries |
| `cm://outcomes` | Session outcomes |
| `cm://stats` | Playbook health metrics |
### Client Configuration
**Claude Code** (`~/.config/claude/mcp.json`):
```json
{
"mcpServers": {
"cm": {
"command": "cm",
"args": ["serve"]
}
}
}
```
---
## Graceful Degradation
| Condition | Behavior |
|-----------|----------|
| No cass | Playbook-only scoring, no history snippets |
| No playbook | Empty playbook, commands still work |
| No LLM | Deterministic reflection, no semantic enhancement |
| Offline | Cached playbook + local diary |
---
## Output Format
All commands support `--json` for machine-readable output.
**Design principle:** stdout = JSON only; diagnostics go to stderr.
### Success Response
```json
{
"success": true,
"task": "fix the auth timeout bug",
"relevantBullets": [
{
"id": "b-8f3a2c",
"content": "Always check token expiry before auth debugging",
"effectiveScore": 8.5,
"maturity": "proven",
"relevanceScore": 0.92,
"reasoning": "Extracted from 5 successful sessions"
}
],
"antiPatterns": [...],
"historySnippets": [...],
"suggestedCassQueries": [...],
"degraded": null
}
```
### Error Response
```json
{
"success": false,
"code": "PLAYBOOK_NOT_FOUND",
"error": "Playbook file not found",
"hint": "Run 'cm init' to create a new playbook",
"retryable": false,
"recovery": ["cm init", "cm doctor --fix"],
"docs": "README.md#-troubleshooting"
}
```
### Exit Codes
| Code | Meaning |
|------|---------|
| 1 | Internal error |
| 2 | User input/usage |
| 3 | Configuration |
| 4 | Filesystem |
| 5 | Network |
| 6 | cass error |
| 7 | LLM/provider error |
---
## Token Budget Management
| Flag | Effect |
|------|--------|
| `--limit N` | Cap number of rules |
| `--min-score N` | Only rules above threshold |
| `--no-history` | Skip historical snippets (faster) |
| `--json` | Structured output |
---
## Configuration
Config lives at `~/.cass-memory/config.json` (global) and `.cass/config.json` (repo).
**Precedence:** CLI flags > Repo config > Global config > Defaults
**Security:** Repo config cannot override sensitive paths or user-level consent settings.
### Key Options
```json
{
"provider": "anthropic",
"model": "claude-sonnet-4-20250514",
"budget": {
"dailyLimit": 0.10,
"monthlyLimit": 2.00
},
"scoring": {
"decayHalfLifeDays": 90,
"harmfulMultiplier": 4
},
"maxBulletsInContext": 50,
"maxHistoryInContext": 10,
"sessionLookbackDays": 7,
"crossAgent": {
"enabled": false,
"consentGiven": false,
"auditLog": true
},
"remoteCass": {
"enabled": false,
"hosts": [{"host": "workstation", "label": "work"}]
},
"semanticSearchEnabled": false,
"embeddingModel": "Xenova/all-MiniLM-L6-v2",
"dedupSimilarityThreshold": 0.85
}
```
### Environment Variables
| Variable | Purpose |
|----------|---------|
| `ANTHROPIC_API_KEY` | API key for Anthropic (Claude) |
| `OPENAI_API_KEY` | API key for OpenAI |
| `GOOGLE_GENERATIVE_AI_API_KEY` | API key for Google Gemini |
| `CASS_PATH` | Path to cass binary |
| `CASS_MEMORY_LLM` | Set to `none` for LLM-free mode |
| `MCP_HTTP_TOKEN` | Auth token for non-loopback MCP server |
---
## Data Locations
```
~/.cass-memory/ # Global (user-level)
├── config.json # Configuration
├── playbook.yaml # Personal playbook
├── diary/ # Session summaries
├── outcomes/ # Session outcomes
├── traumas.jsonl # Trauma patterns
├── starters/ # Custom starter playbooks
├── onboarding-state.json # Onboarding progress
├── privacy-audit.jsonl # Cross-agent audit trail
├── processed-sessions.jsonl # Reflection progress
└── usage.jsonl # LLM cost tracking
.cass/ # Project-level (in repo)
├── config.json # Project-specific overrides
├── playbook.yaml # Project-specific rules
├── traumas.jsonl # Project-specific patterns
└── blocked.yaml # Anti-patterns to block
```
---
## Automating Reflection
### Cron Job
```bash
# Daily at 2am
0 2 * * * /usr/local/bin/cm reflect --days 7 >> ~/.cass-memory/reflect.log 2>&1
```
### Claude Code Hook
`.claude/hooks.json`:
```json
{
"post-session": ["cm reflect --days 1"]
}
```
---
## Privacy & Security
### Local-First Design
- All data stays on your machine
- No cloud sync, no telemetry
- Cross-agent enrichment is opt-in with explicit consent
- Audit log for enrichment events
### Secret Sanitization
Before processing, content is sanitized:
- OpenAI/Anthropic/AWS/Google API keys
- GitHub tokens
- JWTs
- Passwords and secrets in config patterns
### Privacy Controls
```bash
cm privacy status # Check settings
cm privacy enable # Enable cross-agent enrichment
cm privacy disable # Disable enrichment
```
---
## Performance Characteristics
| Operation | Typical Latency |
|-----------|-----------------|
| `cm context` (cached) | 50-150ms |
| `cm context` (cold) | 200-500ms |
| `cm context` (no cass) | 30-80ms |
| `cm reflect` (1 session) | 5-15s |
| `cm reflect` (5 sessions) | 20-60s |
| `cm playbook list` | <50ms |
| `cm similar` (keyword) | 20-50ms |
| `cm similar` (semantic) | 100-300ms |
### LLM Cost Estimates
| Operation | Typical Cost |
|-----------|--------------|
| Reflect (1 session) | $0.01-0.05 |
| Reflect (7 days) | $0.05-0.20 |
| Validate (1 rule) | $0.005-0.01 |
With default budget ($0.10/day, $2.00/month): ~5-10 sessions/day.
---
## Batch Rule Addition
After analyzing a session, add multiple rules at once:
```bash
# Create JSON file
cat > rules.json << 'EOF'
[
{"content": "Always run tests before committing", "category": "testing"},
{"content": "Check token expiry before auth debugging", "category": "debugging"},
{"content": "AVOID: Mocking entire modules in tests", "category": "testing"}
]
EOF
# Add all rules
cm playbook add --file rules.json
# Track which session they came from
cm playbook add --file rules.json --session /path/to/session.jsonl
# Or pipe from stdin
echo '[{"content": "Rule", "category": "workflow"}]' | cm playbook add --file -
```
---
## Template Output for Onboarding
`--template` provides rich context for rule extraction:
```bash
cm onboard read /path/to/session.jsonl --template --json
```
Returns:
- **metadata**: path, workspace, message count, topic hints
- **context**: related rules, playbook gaps, suggested focus
- **extractionFormat**: schema, categories, examples
- **sessionContent**: actual session data
---
## Integration with CASS
CASS provides **episodic memory** (raw sessions).
CM extracts **procedural memory** (rules and playbooks).
```bash
# CASS: Search raw sessions
cass search "authentication timeout" --robot
# CM: Get distilled rules for a task
cm context "authentication timeout" --json
```
---
## Troubleshooting
| Error | Solution |
|-------|----------|
| `cass not found` | Install from [cass repo](https://github.com/Dicklesworthstone/coding_agent_session_search) |
| `cass search failed` | Run `cass index --full` |
| `API key missing` | Set `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, or `GOOGLE_GENERATIVE_AI_API_KEY` |
| `Playbook corrupt` | Run `cm doctor --fix` |
| `Budget exceeded` | Check `cm usage`, adjust limits |
### Diagnostic Commands
```bash
cm doctor --json # System health
cm doctor --fix # Auto-fix issues
cm usage # LLM budget status
cm stats --json # Playbook health
cm why <bullet-id> # Rule provenance
```
### LLM-Free Mode
```bash
CASS_MEMORY_LLM=none cm context "task" --json
```
---
## Installation
```bash
# One-liner (recommended)
curl -fsSL https://raw.githubusercontent.com/Dicklesworthstone/cass_memory_system/main/install.sh \
| bash -s -- --easy-mode --verify
# Specific version
install.sh --version v0.2.2 --verify
# System-wide
install.sh --system --verify
# From source
git clone https://github.com/Dicklesworthstone/cass_memory_system.git
cd cass_memory_system
bun install && bun run build
sudo mv ./dist/cass-memory /usr/local/bin/cm
```
---
## Integration with Flywheel
| Tool | Integration |
|------|-------------|
| **CASS** | CM reads from cass episodic memory, writes procedural memory |
| **NTM** | Robot mode integrates with cm for context before agent work |
| **Agent Mail** | Rules can reference mail threads as provenance |
| **BV** | Task context enriched with relevant playbook rules |Related Skills
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Fix PageSpeed Insights/Lighthouse accessibility "!" errors caused by contrast audit failures (CSS filters, OKLCH/OKLAB, low opacity, gradient text, image backgrounds). Use for accessibility-driven SEO/performance debugging and remediation.
project-to-obsidian
将代码项目转换为 Obsidian 知识库。当用户提到 obsidian、项目文档、知识库、分析项目、转换项目 时激活。 【激活后必须执行】: 1. 先完整阅读本 SKILL.md 文件 2. 理解 AI 写入规则(默认到 00_Inbox/AI/、追加式、统一 Schema) 3. 执行 STEP 0: 使用 AskUserQuestion 询问用户确认 4. 用户确认后才开始 STEP 1 项目扫描 5. 严格按 STEP 0 → 1 → 2 → 3 → 4 顺序执行 【禁止行为】: - 禁止不读 SKILL.md 就开始分析项目 - 禁止跳过 STEP 0 用户确认 - 禁止直接在 30_Resources 创建(先到 00_Inbox/AI/) - 禁止自作主张决定输出位置
obsidian-helper
Obsidian 智能笔记助手。当用户提到 obsidian、日记、笔记、知识库、capture、review 时激活。 【激活后必须执行】: 1. 先完整阅读本 SKILL.md 文件 2. 理解 AI 写入三条硬规矩(00_Inbox/AI/、追加式、白名单字段) 3. 按 STEP 0 → STEP 1 → ... 顺序执行 4. 不要跳过任何步骤,不要自作主张 【禁止行为】: - 禁止不读 SKILL.md 就开始工作 - 禁止跳过用户确认步骤 - 禁止在非 00_Inbox/AI/ 位置创建新笔记(除非用户明确指定)
internationalizing-websites
Adds multi-language support to Next.js websites with proper SEO configuration including hreflang tags, localized sitemaps, and language-specific content. Use when adding new languages, setting up i18n, optimizing for international SEO, or when user mentions localization, translation, multi-language, or specific languages like Japanese, Korean, Chinese.
google-official-seo-guide
Official Google SEO guide covering search optimization, best practices, Search Console, crawling, indexing, and improving website search visibility based on official Google documentation
github-release-assistant
Generate bilingual GitHub release documentation (README.md + README.zh.md) from repo metadata and user input, and guide release prep with git add/commit/push. Use when the user asks to write or polish README files, create bilingual docs, prepare a GitHub release, or mentions release assistant/README generation.
doc-sync-tool
自动同步项目中的 Agents.md、claude.md 和 gemini.md 文件,保持内容一致性。支持自动监听和手动触发。
deploying-to-production
Automate creating a GitHub repository and deploying a web project to Vercel. Use when the user asks to deploy a website/app to production, publish a project, or set up GitHub + Vercel deployment.